Update build_index_live_html.py

build_index_live_html.py

@@ -1,251 +1,4 @@
-'''
-from nsepython import *
-import pandas as pd
-
-def build_index_live_html(name=""):
-    p = nse_index_live(name)
-
-    full_df = p.get("data", pd.DataFrame())
-    rem_df  = p.get("rem", pd.DataFrame())
-
-    if full_df.empty:
-        main_df = pd.DataFrame()
-        const_df = pd.DataFrame()
-    else:
-        main_df = full_df.iloc[[0]]
-        const_df = full_df.iloc[1:]  # Constituents
-        if not const_df.empty:
-            const_df = const_df.iloc[:, 1:]  # Remove first column
-
-            # Columns to move from constituents to info
-            move_to_info = [c for c in ['segment', 'equityTime', 'preOpenTime'] if c in const_df.columns]
-            if move_to_info:
-                rem_df = pd.concat([rem_df, const_df[move_to_info].iloc[[0]]], axis=1)
-                const_df = const_df.drop(columns=move_to_info)
-
-            # Drop unnecessary columns from Constituents
-            drop_cols_const = [
-                "identifier", "ffmc", "stockIndClosePrice", "lastUpdateTime",
-                "chartTodayPath", "chart30dPath", "chart365dPath", "series",
-                "symbol_meta", "activeSeries", "debtSeries", "isFNOSec",
-                "isCASec", "isSLBSec", "isDebtSec", "isSuspended",
-                "tempSuspendedSeries", "isETFSec", "isDelisted",
-                "slb_isin", "isMunicipalBond", "isHybridSymbol", "QuotePreOpenFlag"
-            ]
-            const_df = const_df.drop(columns=[c for c in drop_cols_const if c in const_df.columns])
-
-            # Drop unnecessary columns from Main Data
-            drop_cols_main = [
-                "series", "symbol_meta", "companyName", "industry", "activeSeries", "debtSeries",
-                "isFNOSec", "isCASec", "isSLBSec", "isDebtSec", "isSuspended", "tempSuspendedSeries",
-                "isETFSec", "isDelisted", "isin", "slb_isin", "listingDate", "isMunicipalBond",
-                "isHybridSymbol", "segment", "equityTime", "preOpenTime", "QuotePreOpenFlag"
-            ]
-            main_df = main_df.drop(columns=[c for c in drop_cols_main if c in main_df.columns])
-
-            # Ensure pChange is numeric and sort
-            if 'pChange' in const_df.columns:
-                const_df['pChange'] = pd.to_numeric(const_df['pChange'], errors='coerce')
-                const_df = const_df.sort_values('pChange', ascending=False)
-
-    # ================= HELPER FUNCTION: COLOR-CODE AND FORMAT NUMERIC =================
-    def df_to_html_color(df, metric_col=None):
-        df_html = df.copy()
-        top3_up = []
-        top3_down = []
-        if metric_col and metric_col in df_html.columns and pd.api.types.is_numeric_dtype(df_html[metric_col]):
-            col_numeric = df_html[metric_col].dropna()
-            top3_up = col_numeric.nlargest(3).index.tolist()
-            top3_down = col_numeric.nsmallest(3).index.tolist()
-
-        for idx, row in df_html.iterrows():
-            for col in df_html.columns:
-                val = row[col]
-                style = ""
-                if pd.api.types.is_numeric_dtype(type(val)) or isinstance(val, (int, float)):
-                    val_fmt = f"{val:.2f}"
-                    if val > 0:
-                        style = "numeric-positive"
-                    elif val < 0:
-                        style = "numeric-negative"
-                    if metric_col and col == metric_col:
-                        if idx in top3_up:
-                            style += " top-up"
-                        elif idx in top3_down:
-                            style += " top-down"
-                    df_html.at[idx, col] = f'<span class="{style.strip()}">{val_fmt}</span>'
-                else:
-                    df_html.at[idx, col] = str(val)
-        return df_html.to_html(index=False, escape=False, classes="compact-table")
-
-    rem_html  = df_to_html_color(rem_df)
-    main_html = df_to_html_color(main_df)
-    cons_html = df_to_html_color(const_df)
-
-    # ================= METRIC TABLES =================
-    metric_cols = [
-        "pChange", "totalTradedValue", "nearWKH", "nearWKL",
-        "perChange365d", "perChange30d"
-    ]
-
-    metric_tables = ""
-    for col in metric_cols:
-        if col not in const_df.columns:
-            continue
-
-        df_const = const_df.copy()
-        df_const[col] = pd.to_numeric(df_const[col], errors="ignore")
-        df_const = df_const.sort_values(col, ascending=False)
-        df_html = df_to_html_color(df_const[['symbol', col]], metric_col=col)
-
-        metric_tables += f"""
-        <div class="small-table">
-            <div class="st-title">{col}</div>
-            <div class="st-body">{df_html}</div>
-        </div>
-        """
-
-    # ================= FINAL HTML =================
-    html = f"""
-<!DOCTYPE html>
-<html>
-<head>
-<meta charset="UTF-8">
 
-<style>
-body {{
-    font-family: Arial;
-    margin: 12px;
-    background: #f5f5f5;
-    color: #222;
-    font-size: 14px;
-}}
-
-h2, h3 {{
-    margin: 12px 0 6px 0;
-    font-weight: 600;
-}}
-
-table {{
-    border-collapse: collapse;
-    width: 100%;
-    table-layout: auto;
-}}
-
-th, td {{
-    border: 1px solid #bbb;
-    padding: 5px 8px;
-    text-align: left;
-    font-size: 13px;
-}}
-
-th {{
-    background: #333;
-    color: white;
-    font-weight: 600;
-}}
-
-.compact-table td.numeric-positive {{
-    color: green;
-    font-weight: bold;
-}}
-.compact-table td.numeric-negative {{
-    color: red;
-    font-weight: bold;
-}}
-
-/* Highlight top 3 gainers / losers */
-.compact-table td.top-up {{
-    background: #a8f0a5; /* light green */
-}}
-.compact-table td.top-down {{
-    background: #f0a8a8; /* light red */
-}}
-
-/* Fixed row height & clipping for Constituent Table */
-#constituents-table tr, #constituents-table td {{
-    max-height: 25px;
-    height: 25px;
-    overflow: hidden;
-    white-space: nowrap;
-    text-overflow: ellipsis;
-}}
-
-.small-table {{
-    background: white;
-    border-radius: 6px;
-    padding: 8px;
-    box-shadow: 0px 1px 4px rgba(0,0,0,0.15);
-    border: 1px solid #ddd;
-    overflow-y: auto;
-}}
-
-.st-title {{
-    font-size: 14px;
-    text-align: center;
-    margin-bottom: 6px;
-    font-weight: bold;
-    background: #222;
-    color: white;
-    padding: 5px 0;
-    border-radius: 4px;
-}}
-
-.st-body {{
-    max-height: 300px;  /* vertical scroll for metric tables */
-    overflow-y: auto;
-    font-size: 12px;
-}}
-
-.compact-section {{
-    background: white;
-    padding: 8px;
-    border-radius: 6px;
-    box-shadow: 0 1px 4px rgba(0,0,0,0.12);
-    border: 1px solid #ddd;
-    margin-bottom: 15px;
-    overflow-x: visible;
-}}
-
-.grid {{
-    display: grid;
-    grid-template-columns: repeat(5, 1fr);
-    gap: 12px;
-    margin-top: 12px;
-}}
-</style>
-</head>
-<body>
-
-<h2>Live Index Data: {name or 'Default Index'}</h2>
-
-<div class="compact-section">
-    <h3>Index Info</h3>
-    {rem_html}
-</div>
-
-<div class="compact-section">
-    <h3>Main Data</h3>
-    {main_html}
-</div>
-
-<div class="compact-section">
-    <h3>Constituents</h3>
-    <div id="constituents-table">
-        {cons_html}
-    </div>
-</div>
-
-<h3>Metric Tables (All Symbols)</h3>
-<div class="grid">
-    {metric_tables}
-</div>
-
-</body>
-</html>
-"""
-    return html
-'''
 from nsepython import *
 import pandas as pd